Autism Spectrum Disorder (ASD or autism) is a phenotypically and etiologically heterogeneous condition. Identifying biomarkers of clinically significant metabolic subtypes of autism could improve understanding of its underlying pathophysiology and potentially lead to more targeted interventions. We hypothesized that the application of metabolite-based biomarker techniques using decision thresholds derived from quantitative measurements could identify autism-associated subpopulations. Metabolomic profiling was carried out in a case–control study of 499 autistic and 209 typically developing (TYP) children, ages 18–48 months, enrolled in the Children’s Autism Metabolome Project (CAMP; ClinicalTrials.gov Identifier: NCT02548442). Fifty-four metabolites, associated with amino acid, organic acid, acylcarnitine and purine metabolism as well as microbiome-associated metabolites, were quantified using liquid chromatography-tandem mass spectrometry. Using quantitative thresholds, the concentrations of 4 metabolites and 149 ratios of metabolites were identified as biomarkers, each identifying subpopulations of 4.5–11% of the CAMP autistic population. A subset of 42 biomarkers could identify CAMP autistic individuals with 72% sensitivity and 90% specificity. Many participants were identified by several metabolic biomarkers. Using hierarchical clustering, 30 clusters of biomarkers were created based on participants’ biomarker profiles. Metabolic changes associated with the clusters suggest that altered regulation of cellular metabolism, especially of mitochondrial bioenergetics, were common metabolic phenotypes in this cohort of autistic participants. Autism severity and cognitive and developmental impairment were associated with increased lactate, many lactate containing ratios, and the number of biomarker clusters a participant displayed. These studies provide evidence that metabolic phenotyping is feasible and that defined autistic subgroups can lead to enhanced understanding of the underlying pathophysiology and potentially suggest pathways for targeted metabolic treatments.

Autism Spectrum Disorder (ASD or autism) is a clinically and etiologically heterogeneous condition. Stratification of autistic individuals into subpopulations with shared metabolic phenotypes can improve understanding of the underlying pathophysiology leading to more precise interventions. We performed metabolomic profiling of 499 autistic and 209 typically developing children, ages 18-48 months, enrolled in the Children’s Autism Metabolome Project (CAMP). Through application of plasma metabolomic analyses of a large population of autistic and typically developing children, individuals were stratified using metabolic phenotypes (metabotypes). Metabotypes associated with autism provide insight into the heterogeneity of this condition. Fifty-four metabolites, mainly associated with amino acid, organic acid, acylcarnitine, and purine metabolism, were quantified using liquid chromatography-tandem mass spectrometry, including important metabolites and ratios related to energy homeostasis not evaluated in earlier studies. Quantitative thresholds of the concentrations of 4 metabolites and 149 ratios of concentrations of metabolites were identified that could stratify CAMP participants into metabolic subpopulations of primarily autistic individuals. One or more metabotypes were present in 83% of the autistic participants. based on the similarity of metabotype profiles of the participants. The metabotypes were grouped into 30 clusters based on the similarity of metabotype profiles of the participants. The clusters displayed 7 broader metabolic patterns of changes in metabolites associated with glycolysis, tricarboxylic acid cycle (TCA), amino acid, purine, and acylcarnitine metabolism. Metabolic phenotypes related to cellular bioenergetics, especially mitochondrial dysmetabolism, were commonly observed in this large cohort of autistic participants. A subset of 42 metabotypes could identify CAMP autistic individuals with 72% sensitivity and 90% specificity. As the number of metabotype clusters increased in each individual, so did the autism severity. Metabolic profiling of autistic children can support diagnosis, enhance understanding of the underlying pathophysiology and suggest targeted metabolic treatments.

Identification of early biomarkers of heart injury and drug-induced cardiotoxicity is important to eliminate harmful drug candidates early in preclinical development and to prevent severe drug effects. The main objective of this study was to investigate the expression of microRNAs (miRNAs) in human-induced pluripotent stem cell cardiomyocytes (hiPSC-CM) in response to a broad range of cardiotoxic drugs. Next generation sequencing was applied to hiPSC-CM treated for 72 h with 40 drugs falling into the categories of functional (i.e., ion channel blockers), structural (changes in cardiomyocytes structure), and general (causing both functional and structural) cardiotoxicants as well as non-cardiotoxic drugs. The largest changes in miRNAs expression were observed after treatments with structural or general cardiotoxicants. The number of deregulated miRNAs was the highest for idarubicin, mitoxantrone, and bortezomib treatments. RT-qPCR validation confirmed upregulation of several miRNAs across multiple treatments at therapeutically relevant concentrations: hsa-miR-187-3p, hsa-miR-146b-5p, hsa-miR-182-5p (anthracyclines); hsa-miR-365a-5p, hsa-miR-185-3p, hsa-miR-184, hsa-miR-182-5p (kinase inhibitors); hsa-miR-182-5p, hsa-miR-126-3p and hsa-miR-96-5p (common some anthracyclines, kinase inhibitors and bortezomib). Further investigations showed that an upregulation of hsa-miR-187-3p and hsa-miR-182-5p could serve as a potential biomarker of structural cardiotoxicity and/or an additional endpoint to characterize cardiac injury in vitro.

Autism spectrum disorder (ASD) is biologically and behaviorally heterogeneous. Delayed diagnosis of ASD is common and problematic. The complexity of ASD and the low sensitivity of available screening tools are key factors in delayed diagnosis. Identification of biomarkers that reduce complexity through stratification into reliable subpopulations can assist in earlier diagnosis, provide insight into the biology of ASD, and potentially suggest targeted interventions. Quantitative metabolomic analysis was performed on plasma samples from 708 fasting children, aged 18 to 48 months, enrolled in the Children's Autism Metabolome Project (CAMP). The primary goal was to identify alterations in metabolism helpful in stratifying ASD subjects into subpopulations with shared metabolic phenotypes (i.e., metabotypes). Metabotypes associated with ASD were identified in a discovery set of 357 subjects. The reproducibility of the metabotypes was validated in an independent replication set of 351 CAMP subjects. Thirty‐four candidate metabotypes that differentiated subsets of ASD from typically developing participants were identified with sensitivity of at least 5% and specificity greater than 95%. The 34 metabotypes formed six metabolic clusters based on ratios of either lactate or pyruvate, succinate, glycine, ornithine, 4‐hydroxyproline, or α‐ketoglutarate with other metabolites. Optimization of a subset of new and previously defined metabotypes into a screening battery resulted in 53% sensitivity (95% confidence interval [CI], 48%–57%) and 91% specificity (95% CI, 86%–94%). Thus, our metabolomic screening tool detects more than 50% of the autistic participants in the CAMP study. Further development of this metabolomic screening approach may facilitate earlier referral and diagnosis of ASD and, ultimately, more targeted treatments.Lay SummaryAnalysis of a selected set of metabolites in blood samples from children with autism and typically developing children identified reproducible differences in the metabolism of about half of the children with autism. Testing for these differences in blood samples can be used to help screen children as young as 18 months for risk of autism that, in turn, can facilitate earlier diagnoses. In addition, differences may lead to biological insights that produce more precise treatment options. We are exploring other blood‐based molecules to determine if still a higher percentage of children with autism can be detected using this strategy. Autism Res 2020, 13: 1270–1285. © 2020 The Authors. Autism Research published by International Society for Autism Research published by Wiley Periodicals LLC.

Implementing screening assays that identify functional and structural cardiotoxicity earlier in the drug development pipeline has the potential to improve safety and decrease the cost and time required to bring new drugs to market. In this study, a metabolic biomarker-based assay was developed that predicts the cardiotoxicity potential of a drug based on changes in the metabolism and viability of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CM). Assay development and testing was conducted in 2 phases: (1) biomarker identification and (2) targeted assay development. In the first phase, metabolomic data from hiPSC-CM spent media following exposure to 66 drugs were used to identify biomarkers that identified both functional and structural cardiotoxicants. Four metabolites that represent different metabolic pathways (arachidonic acid, lactic acid, 2'-deoxycytidine, and thymidine) were identified as indicators of cardiotoxicity. In phase 2, a targeted, exposure-based biomarker assay was developed that measured these metabolites and hiPSC-CM viability across an 8-point concentration curve. Metabolite-specific predictive thresholds for identifying the cardiotoxicity potential of a drug were established and optimized for balanced accuracy or sensitivity. When predictive thresholds were optimized for balanced accuracy, the assay predicted the cardiotoxicity potential of 81 drugs with 86% balanced accuracy, 83% sensitivity, and 90% specificity. Alternatively, optimizing the thresholds for sensitivity yields a balanced accuracy of 85%, 90% sensitivity, and 79% specificity. This new hiPSC-CM-based assay provides a paradigm that can identify structural and functional cardiotoxic drugs that could be used in conjunction with other endpoints to provide a more comprehensive evaluation of a drug's cardiotoxicity potential.

Our article, entitled “Amino Acid Dysregulation Metabotypes: Potential Biomarkers for Diagnosis and Individualized Treatment for Subtypes of Autism Spectrum Disorder,” provides an important step toward establishing a reliable biological marker of increased risk for a diagnosis of autism spectrum disorder (ASD) ( 1 Smith A.M. King J.J. West P.R. Ludwig M.A. Donley E.L.R. Burrier R.E. et al. Amino acid dysregulation metabotypes: Potential biomarkers for diagnosis and individualized treatment for subtypes of autism spectrum disorder. Biol Psychiatry. 2019; 85: 345-354 Abstract Full Text Full Text PDF PubMed Scopus (67) Google Scholar ). This publication is based on data from the Children’s Autism Metabolome Project (CAMP) (NCT02548442), which has enrolled 1100 children with ASD, developmental delay, and typical development. This study had the goal of determining whether a subset of children with ASD had altered branch chain amino acid metabolism. The conclusion is that approximately 17% of children with ASD demonstrate such an imbalance of these amino acids. The article has been criticized by Sainani and Goodman ( 2 Sainani K.L. Goodman S.N. Lack of diagnostic utility of “amino acid dysregulation metabotypes.”. Biol Psychiatry. 2019; 85: e41-e42 Scopus (2) Google Scholar ). We believe that these criticisms are based on a lack of understanding of the state of biomarker research in ASD and specifically on the iterative, multistep process that research on biomarkers must take to establish a diagnostic test. We address each of their concerns. Lack of Diagnostic Utility of “Amino Acid Dysregulation Metabotypes”Biological PsychiatryVol. 85Issue 7PreviewWe read with interest the article by Smith et al. (1) on amino acid dysregulation metabotypes (AADMs). The hypothesis that there may be subtypes of autism spectrum disorder (ASD) marked by measurable metabolic changes is interesting. However, we believe that the data presented in the article demonstrate that the test described has minimal or no diagnostic utility. Here, we describe five serious problems that led to this conclusion. Full-Text PDF

Background Autism spectrum disorder (ASD) is behaviorally and biologically heterogeneous and likely represents a series of conditions arising from different underlying genetic, metabolic, and environmental factors. There are currently no reliable diagnostic biomarkers for ASD. Based on evidence that dysregulation of branched-chain amino acids (BCAAs) may contribute to the behavioral characteristics of ASD, we tested whether dysregulation of amino acids (AAs) was a pervasive phenomenon in individuals with ASD. This is the first article to report results from the Children's Autism Metabolome Project (CAMP), a large-scale effort to define autism biomarkers based on metabolomic analyses of blood samples from young children. Methods Dysregulation of AA metabolism was identified by comparing plasma metabolites from 516 children with ASD with those from 164 age-matched typically developing children recruited into the CAMP. ASD subjects were stratified into subpopulations based on shared metabolic phenotypes associated with BCAA dysregulation. Results We identified groups of AAs with positive correlations that were, as a group, negatively correlated with BCAA levels in ASD. Imbalances between these two groups of AAs identified three ASD-associated amino acid dysregulation metabotypes. The combination of glutamine, glycine, and ornithine amino acid dysregulation metabotypes identified a dysregulation in AA/BCAA metabolism that is present in 16.7% of the CAMP subjects with ASD and is detectable with a specificity of 96.3% and a positive predictive value of 93.5% within the ASD subject cohort. Conclusions Identification and utilization of metabotypes of ASD can lead to actionable metabolic tests that support early diagnosis and stratification for targeted therapeutic interventions.

Objective: To identify and prospectively validate metabolite-based diagnostic tests for subtypes as subpopulations of ASD and developmental delay (DD). Background: Underlying factors of autism spectrum disorder (ASD) lead to individual differences in response to therapies. Earlier diagnosis of children with ASD improves outcomes through early initiation of interventions. Based on previous metabolic profiling of ASD in over 500 subjects, we postulated that metabolites can define subtypes of ASD. Design/Methods: Diagnosis is based on DSM-5 and ADOS for ASD and MSEL for DD. Plasma was analyzed using a quantitative assay for a panel of amine-containing metabolites. Samples from 368 subjects (242 ASD, 40 DD, 87 typically-developing), 18–48 months old, were selected to set thresholds for each subtype diagnostic. Over 300 subjects not previously measured were analyzed to determine each subtype diagnostic test’s clinical performance. We designed the Children’s Autism Metabolome Project (CAMP, ClinicalTrials.gov Identifier NCT02548442), the largest metabolomics study of autism (1,000+ pediatric subjects), based on our findings. Metabolomic analyses of fasting blood samples and validated subtypes indicate potentially actionable results. Metabolic subtypes may identify subjects who could benefit from specific dietary and pharmacological interventions. Results: Thresholds were set for subtype diagnostic tests for ASD or DD, and for a subtype diagnostic for the diagnosis of ASD. The prospective analysis, to be completed before this presentation, is expected to validate several molecular subtypes. Conclusions: CAMP provides the largest set of samples collected for the investigation of metabolic differences associated with ASD and DD. This report focuses on the validation of molecular subtype diagnostic tests that reliably identify ASD and DD children in a prospective analysis. Each subtype test suggests an imbalance of metabolites that points towards possible interventions to improve outcomes. Study Supported by: National Institute of Mental Health and Nancy Lurie Marks Family Foundation Disclosure: Dr. Donley has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities with Stemina Biomarker Discovery, Inc. Dr. Donley holds stock and/or stock options in Stemina Biomarker Discovery, Inc., which sponsored research in which Dr. Donley was involved as an investigator. Dr. Burrier has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities with Stemina Biomarker Discovery, Inc. Dr. King has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities with Stemina Biomarker Discovery, Inc. Dr. Smith has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities with Stemina Biomarker Discovery, Inc. Dr. West has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities with Stemina Biomarker Discovery, Inc. Dr. Feuling has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities with Stemina Biomarker Discovery, Inc. Dr. Ludwig has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities with Stemina Biomarker Discovery, Inc. Dr. Sugden has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities with Stemina Biomarker Discovery, Inc. Dr. Smart has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities with Stemina Biomarker Discovery, Inc. Dr. Amaral has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities with UC - Davis.

The relative developmental toxicity potency of a series of retinoid analogues was evaluated using a human induced pluripotent stem (iPS) cell assay that measures changes in the biomarkers ornithine and cystine. Analogue potency was predicted, based on the assay endpoint of the ornithine/cystine (o/c) ratio, to be all-trans-retinoic acid>TTNPB>13-cis-retinoic acid≈9-cis-retinoic acid>acitretin>etretinate>retinol. These rankings correlate with in vivo data and demonstrate successful application of the assay to rank a series of related toxic and non-toxic compounds. The retinoic acid receptor α (RARα)-selective antagonist Ro 41-5253 inhibited the cystine perturbation caused by all-trans-retinoic acid, TTNPB, 13-cis-retinoic acid, 9-cis-retinoic acid, and acitretin. Ornithine was altered independent of RARα in all retinoids except acitretin. These results suggest a role for an RARα-mediated mechanism in retinoid-induced developmental toxicity through altered cystine metabolism.

This review summarizes the report entitled: Breast Cancer and the Environment: Prioritizing Prevention, highlights research gaps and the importance of focusing on early life exposures for breast development and breast cancer risk.

This chapter discusses the current tests used to identify developmental toxicity potential, the need for new testing strategies, technologies that can be used to develop these strategies, and the alternatives in development that are based on human embryonic stem (hES) cells. Pluripotent stem (PS) cell research dates back almost 40 years to the early 1970s, when the first mouse embryonic carcinoma (EC) cell lines were established. Metabolites are generated from the action of enzymes and are the intermediates or end products of cellular regulatory processes. The basis of using embryonic stem cells for developmental toxicity testing was pioneered in the 1990s with the development of the mouse embryonic stem cell test (mEST). Metabolomics and hPS cell-based assays, independently, are exciting new approaches that can advance the field of developmental toxicity screening toward the directive put forth by the NRC's Tox21c report.

: Current literature suggests diverse toxicological consequences for methyl parathion (MP) and its active metabolite, methyl paraoxon (MPO) exposure. These studies, however, have been based on either human epidemiological studies, in vivo animal studies, or in vitro studies using immortal cell lines. Currently, there remains no definitive connection to the molecular events that occur during MP and MPO exposure in normal human cells. Furthermore, chemicals that have certain known effects in adults can have dramatically different toxic effects during embryonic and prenatal development. Since undifferentiated human embryonic stem cells (hESC) maintain the ability to differentiate into any somatic cell in the body, they provide a unique window into the influence of toxicants on the entire early human development. The purpose of this study was to perform initial nontargeted metabolomic analysis on hESC exposed to MP and MPO to identify human metabolites and metabolic pathways which are perturbed following exposure to these chemicals. From this study, two main conclusions were reached: (1) hESC are not able to metabolize MP into MPO, and (2) MPO is much more disruptive to key developmental metabolic pathways at physiologically relevant concentrations than is MP.

Background The diagnosis of autism spectrum disorder (ASD) at the earliest age possible is important for initiating optimally effective intervention. In the United States the average age of diagnosis is 4 years. Identifying metabolic biomarker signatures of ASD from blood samples offers an opportunity for development of diagnostic tests for detection of ASD at an early age. Objectives To discover metabolic features present in plasma samples that can discriminate children with ASD from typically developing (TD) children. The ultimate goal is to identify and develop blood-based ASD biomarkers that can be validated in larger clinical trials and deployed to guide individualized therapy and treatment. Methods Blood plasma was obtained from children aged 4 to 6, 52 with ASD and 30 age-matched TD children. Samples were analyzed using 5 mass spectrometry-based methods designed to orthogonally measure a broad range of metabolites. Univariate, multivariate and machine learning methods were used to develop models to rank the importance of features that could distinguish ASD from TD. Results A set of 179 statistically significant features resulting from univariate analysis were used for multivariate modeling. Subsets of these features properly classified the ASD and TD samples in the 61-sample training set with average accuracies of 84% and 86%, and with a maximum accuracy of 81% in an independent 21-sample validation set. Conclusions This analysis of blood plasma metabolites resulted in the discovery of biomarkers that may be valuable in the diagnosis of young children with ASD. The results will form the basis for additional discovery and validation research for 1) determining biomarkers to develop diagnostic tests to detect ASD earlier and improve patient outcomes, 2) gaining new insight into the biochemical mechanisms of various subtypes of ASD 3) identifying biomolecular targets for new modes of therapy, and 4) providing the basis for individualized treatment recommendations.